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Acta Oceanologica Sinica 2017年第8期36卷 34-41页

作者： song mirong LIU Jiping State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China

Large parts of North America, Europe, Siberia, and East Asia have experienced cold snaps and heavy snowfalls for the past few winters, which have been linked to rapid decline of Arctic sea ice. Although the role of reduction in Arctic sea ice in recent cold and snowy winters is still a matter of debate, there is considerable interest in determining whether such an emerging climate feedback will persist into the future in a warming environment. Here we show that increased winter snowfall would be a robust feature throughout the 21st century in the northeastern Europe, central and northern Asia and northern North America as projected by current-day climate model simulations under the medium mitigation scenario. We argue that the increased winter snowfall in these regions during the 21st century is due primarily to the diminishing autumn Arctic sea ice （largely externally forced）. Variability of the winter Arctic Oscillation （dominant mode of natural variability in the Northern Hemisphere）, in contrast, has little contribution to the increased winter snowfall. This is evident in not only the multi-model ensemble mean, but also each individual model （not model-dependent）. Our findings reinforce suggestions that a strong sea ice-snowfall feedback might have emerged, and would be enhanced in coming decades, increasing the chance of heavy snowfall events in northern high-latitude continents.

关键词： Arctic sea ice winter snowfall warming environment

Quantifying the contribution of natural variability to September Arctic sea ice decline

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Acta Oceanologica Sinica 2016年第5期35卷 49-53页

作者： song mirong WEI Lixin WANG Zhenzhan State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of Sciences Key Laboratory of Research on Marine Hazards Forecasting National Marine Environmental Forecasting CenterState Oceanic Administration Key Laboratory of Microwave Remote Sensing National Space Science CenterChinese Academy of Sciences

Arctic sea ice extent has been declining in recent decades. There is ongoing debate on the contribution of natural internal variability to recent and future Arctic sea ice changes. In this study, we contrast the trends in the forced and unforced simulations of carefully selected global climate models with the extended observed Arctic sea ice records. The results suggest that the natural variability explains no more than 42.3% of the observed September sea ice extent trend during 35 a（1979–2013） satellite observations, which is comparable to the results of the observed sea ice record extended back to 1953（61 a, less than 48.5% natural variability）. This reinforces the evidence that anthropogenic forcing plays a substantial role in the observed decline of September Arctic sea ice in recent decades. The magnitude of both positive and negative trends induced by the natural variability in the unforced simulations is slightly enlarged in the context of increasing greenhouse gases in the 21st ***, the ratio between the realizations of positive and negative trends change has remained steady, which enforces the standpoint that external forcing will remain the principal determiner of the decreasing Arctic sea ice extent trend in the future.

关键词： internal variability sea ice decline external forcing

Sensitivity and nonlinearity of Eurasian winter temperature response to recent Arctic sea ice loss

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Acta Oceanologica Sinica 2017年第8期36卷 52-58页

作者： SUI Cuijuan ZHANG Zhanhai YU Lejiang LI Yi song mirong College of Physical and Environmental Oceanography Ocean University of ChinaQingdao 266100China National Marine Environmental Forecasting Center Beijing 100081China Polar Research Institute of China Shanghai 200129China Institute of Atmospheric Physics Chinese Academy of SciencesBeijing 100029China

The recent decline in the Arctic sea ice has coincided with more cold winters in Eurasia. It has been hypothesized that the Arctic sea ice loss is causing more mid-latitude cold extremes and cold winters, yet there is lack of consensus in modeling studies on the impact of Arctic sea ice loss. Here we conducted modeling experiments with Community Atmosphere Model Version 5 （CAM5） to investigate the sensitivity and linearity of Eurasian winter temperature response to the Atlantic sector and Pacific sector of the Arctic sea ice loss. Our experiments indicate that the Arctic sea ice reduction can significantly affect the atmospheric circulation by strengthening the Siberian High, exciting the stationary Rossby wave train, and weakening the polar jet stream, which in turn induce the cooling in Eurasia. The temperature decreases by more than I^C in response to the ice loss in the Atlantic sector and the cooling is less and more shifts southward in response to the ice loss in the Pacific sector. More interestingly, sea ice loss in the Atlantic and Pacific sectors together barely induces cold temperatures in Eurasia, suggesting the nonlinearity of the atmospheric response to the Arctic sea ice loss.

关键词： cold winter CAM5 sensitivity experiment jet stream

Investigation of Arctic air temperature extremes at north of 60°N in winter

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Acta Oceanologica Sinica 2017年第11期36卷 51-60页

作者： SUI Cuijuan ZHANG Zhanhai YU Lejiang LI Yi song mirong College of Physical and Environmental Oceanography Ocean University of China Qingdao 266100 China National Marine Environmental Forecasting Center State Oceanic Administration Beijing 100081 China Polar Research Institute of China Shanghai 200129 China Key Laboratory for Polar Science Polar Research Institute of China State Oceanic Administration Shanghai 200136 China Institute of Atmospheric Physics Chinese Academy of Sciences Beijing 100029 China

Air temperature is a key index reflecting climate change. Air temperature extremes are very important because they strongly influence the natural environment and societal activities. The Arctic air temperature extremes north of 60°N are investigated in the winter. Daily data from 238 stations at north of 60°N from the global summary of the day for the period 1979–2015 are used to study the trends of cold days, cold nights, warm days and warm nights during the wintertime. The results show a decreasing trend of cold days and nights（rate of –0.2 to –0.3 d/a） and an increasing trend of warm days and nights（rate of ＋0.2 to ＋0.3 d/a） in the Arctic. The mean temperature increases,which contributes to the increasing（decreasing） occurrence of warm（cold） days and nights. On the other hand,the variance at most stations decreased, leading to a reduced number of cold events. A positive AO（Arctic Oscillation） index leads to an increased（decreased） number of warm（cold） days and nights over northern Europe and western Russia and an increased（decreased） number of cold（warm） days and nights over the Bering Strait and Greenland. The lower extent of Arctic autumn sea ice leads to a decreased number of cold days and *** occurrences of abrupt changes are detected using the Mann-Kendall method for cold nights occurring in Canada in 1998 and for warm nights occurring in northwestern Eurasia in 1988. This abrupt change mainly resulted from the mean warming induced by south winds and an increased North Atlantic sea surface temperature.

关键词： extreme temperature events trend analysis abrupt change analysis composite analysis

CAS-FGOALS Datasets for the Two Interglacial Epochs of the Holocene and the Last Interglacial in PMIP4

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Advances in Atmospheric Sciences 2020年第10期37卷 1034-1044页

作者： Weipeng ZHENG Yongqiang YU Yihua LUAN Shuwen ZHAO Bian HE Li DONG mirong song Pengfei LIN and Hailong LIU State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China College of Earth and Planetary Sciences University of Chinese Academy of SciencesBeijing 100049China Climate Change Research Center Chinese Academy of SciencesBeijing 100029China Chinese Academy of Sciences Center for Excellence in Tibetan Plateau Earth Sciences Beijing 100101China

Two versions of the Chinese Academy of Sciences Flexible Global Ocean-Atmosphere-Land System model(CASFGOALS),version f3-L and g3,are used to simulate the two interglacial epochs of the mid-Holocene and the Last Interglacial in phase 4 of the Paleoclimate Modelling Intercomparison Project(PMIP4),which aims to study the impact of changes in orbital parameters on the Earth’s *** the PMIP4 experimental protocols,four simulations for the mid-Holocene and two simulations for the Last Interglacial have been completed,and all the data,including monthly and daily outputs for the atmospheric,oceanic,land and sea-ice components,have been released on the Earth System Grid Federation(ESGF)*** datasets contribute to PMIP4 and CMIP6(phase 6 of the Coupled Model Intercomparison Project)by providing the variables necessary for the two interglacial *** this paper,the basic information of the CAS-FGOALS models and the protocols for the two interglacials are briefly described,and the datasets are validated using proxy *** suggest that the CAS-FGOALS models capture the large-scale changes in the climate system in response to changes in solar insolation during the interglacial epochs,including warming in mid-to-high latitudes,changes in the hydrological cycle,the seasonal variation in the extent of sea ice,and the damping of interannual variabilities in the tropical ***,disagreements within and between the models and the proxy data are also *** datasets will help the modeling and the proxy data communities with a better understanding of model performance and biases in paleoclimate simulations.

关键词： PMIP4 CMIP6 CAS-FGOALS interglacials orbital parameters

Subseasonal to seasonal Arctic sea-ice prediction:A grand challenge of climate science

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Atmospheric and Oceanic Science Letters 2021年第4期14卷 19-21页

作者： Ke Wei Jiping Liu Qing Bao Bian He Jiao Ma Ming Li mirong song Zhu Zhu Center for Monsoon System Research Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina Department of Atmospheric and Environmental Sciences University at AlbanyAlbanyNYUSA State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina University of Chinese Academy of Sciences BeijingChina Polar Research&Forecasting Division National Marine Environmental Forecasting CenterBeijingChina

On 15 September 2020,the Arctic sea-ice extent(SIE)reached its annual minimum,which,based on data from the National Snow and Ice Data Center(NSIDC,2020a),was about 3.74 million km^(2)(1.44 million square miles).This value was about 40%less than the climate average(~6.27 million km^(2))during 1980–*** was second only to the record low(3.34 million km^(2))set on 16 September 2012,but significantly smaller than the previous second-lowest(4.145 million km^(2),set on 7 September 2016)and third-lowest(4.147 million km^(2),set on 14 September 2007)values,making 2020 the second-lowest SIE year of the satellite era(42 years of data).

关键词： Arctic seasonal climate

Towards reliable Arctic sea ice prediction using multivariate data assimilation

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Science Bulletin 2019年第1期64卷 63-72页

作者： Jiping Liu Zhiqiang Chen Yongyun Hu Yuanyuan Zhang Yifan Ding Xiao Cheng Qinghua Yang Lars Nerger Gunnar Spreen Radley Horton Jun Inoue Chaoyuan Yang Ming Li mirong song Department of Atmospheric and Environmental Sciences University at Albany State University of New York College of Ocean and Meteorology Guangdong Ocean University Department of Atmospheric and Oceanic Sciences School of Physics Peking University College of Global Change and Earth System Science Beijing Normal University Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies and School of Atmospheric Sciences Sun Yat-sen University Alfred-Wegener-Institut Helmholtz Zentrum für Polar-und Meeresforschung University of Bremen Institute of Environmental Physics Lamont-Doherty Earth Observatory Columbia University Earth Institute National Institute of Polar Research Polar Research and Forecasting Division National Marine Environmental Forecasting Center State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric Physics Chinese Academy of Sciences

Rapid declines in Arctic sea ice have captured attention and pose significant challenges to a variety of stakeholders. There is a rising demand for Arctic sea ice prediction at daily to seasonal time scales, which is partly a sea ice initial condition problem. Thus, a multivariate data assimilation that integrates sea ice observations to generate realistic and skillful model initialization is needed to improve predictive skill of Arctic sea ice. Sea ice data assimilation is a relatively new research area. In this review paper, we focus on two challenges for implementing multivariate data assimilation systems for sea ice forecast. First, to address the challenge of limited spatiotemporal coverage and large uncertainties of observations, we discuss sea ice parameters derived from satellite remote sensing that(1) have been utilized for improved model initialization, including concentration, thickness and drift, and(2) are currently under development with the potential for enhancing the predictability of Arctic sea ice, including melt ponds and sea ice leads. Second, to strive to generate the ‘‘best" estimate of sea ice initial conditions by combining model simulations/forecasts and observations, we review capabilities and limitations of different data assimilation techniques that have been developed and used to assimilate observed sea ice parameters in dynamical models.

关键词： Arctic sea ice prediction Remote sensing Data assimilation

CAS-ESM2.0 Model Datasets for the CMIP6 Ocean Model Intercomparison Project Phase 1 (OMIP1)

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Advances in Atmospheric Sciences 2021年第2期38卷 307-316页

作者： Xiao DONG Jiangbo JIN Hailong LIU He ZHANG Minghua ZHANG Pengfei LIN Qingcun ZENG Guangqing ZHOU Yongqiang YU mirong song Zhaohui LIN Ruxu LIAN Xin GAO Juanxiong HE Dongling ZHANG Kangjun CHEN International Center for climate and Environment Sciences Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China College of Earth and Planetary Sciences University of Chinese Academy of SciencesBeijing 100049China Institute of Atmospheric Physics Chinese Academy of SciencesBeijing 100029China

As a member of the Chinese modeling groups,the coupled ocean-ice component of the Chinese Academy of Sciences’Earth System Model,version 2.0(CAS-ESM2.0),is taking part in the Ocean Model Intercomparison Project Phase 1(OMIP1)experiment of phase 6 of the Coupled Model Intercomparison Project(CMIP6).The simulation was conducted,and monthly outputs have been published on the ESGF(Earth System Grid Federation)data *** this paper,the experimental dataset is introduced,and the preliminary performances of the ocean model in simulating the global ocean temperature,salinity,sea surface temperature,sea surface salinity,sea surface height,sea ice,and Atlantic Meridional Overturning Circulation(AMOC)are *** results show that the model is at quasi-equilibrium during the integration of 372 years,and performances of the model are reasonable compared with *** dataset is ready to be downloaded and used by the community in related research,e.g.,multi-ocean-sea-ice model performance evaluation and interannual variation in oceans driven by prescribed atmospheric forcing.

关键词： OGCM CMIP6 OMIP1 AMOC ocean temperature/salinity sea ice

CAS-ESM2.0 Model Datasets for the CMIP6 Flux-Anomaly-Forced Model Intercomparison Project (FAFMIP)

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Advances in Atmospheric Sciences 2021年第2期38卷 296-306页

作者： Jiangbo JIN He ZHANG Xiao DONG Hailong LIU Minghua ZHANG Xin GAO Juanxiong HE Zhaoyang CHAI Qingcun ZENG Guangqing ZHOU Zhaohui LIN Yi YU Pengfei LIN Ruxu LIAN Yongqiang YU mirong song Dongling ZHANG International Center for Climate and Environment Sciences Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China State Key Laboratory of Satellite Ocean Environment Dynamics Second Institute of OceanographyMinistry of Natural ResourcesHangzhou 310012China State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China College of Earth and Planetary Sciences University of Chinese Academy of SciencesBeijing 100049China Institute of Atmospheric Physics Chinese Academy of SciencesBeijing 100029China

The second version of the Chinese Academy of Sciences Earth System Model(CAS-ESM2.0)is participating in the Flux-Anomaly-Forced Model Intercomparison Project(FAFMIP)experiments in phase 6 of the Coupled Model Intercomparison Project(CMIP6).The purpose of FAFMIP is to understand and reduce the uncertainty of ocean climate changes in response to increased CO2 forcing in atmosphere-ocean general circulation models(AOGCMs),including the simulations of ocean heat content(OHC)change,ocean circulation change,and sea level rise due to thermal *** experiments(including faf-heat,faf-stress,faf-water,faf-all,faf-passiveheat,faf-heat-NA50pct and faf-heat-NA0pct)have been *** of the experiments were integrated over a 70-year period and the corresponding data have been uploaded to the Earth System Grid Federation data server for CMIP6 users to *** paper describes the experimental design and model datasets and evaluates the preliminary results of CAS-ESM2.0 simulations of ocean climate changes in the FAFMIP *** simulations of the changes in global ocean temperature,Atlantic Meridional Overturning Circulation(AMOC),OHC,and dynamic sea level(DSL),are all reasonably reproduced.

关键词： CAS-ESM2.0 CMIP6 FAFMIP AMOC ocean heat uptake dynamic sea level change

CAS FGOALS-g3 Model Datasets for the CMIP6 Scenario Model Intercomparison Project(ScenarioMIP)

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Advances in Atmospheric Sciences 2020年第10期37卷 1081-1092页

作者： Ye PU Hongbo LIU Ruojing YAN Hao YANG Kun XIA Yiyuan LI Li DONG Lijuan LI He WANG Yan NIE mirong song Jinbo XIE Shuwen ZHAO Kangjun CHEN Bin WANG Jianghao LI and Ling ZUO State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics(LASG) Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China College of Earth and Planetary Sciences University of Chinese Academy of SciencesBeijing 100049China Department of Atmospheric Sciences Yunnan UniversityKunming 650504China

This paper describes the datasets from the Scenario Model Intercomparison Project(ScenarioMIP)simulation experiments run with the Chinese Academy of Sciences Flexible Global Ocean–Atmosphere–Land System Model,GridPoint version 3(CAS FGOALS-g3).FGOALS-g3 is driven by eight shared socioeconomic pathways(SSPs)with different sets of future emission,concentration,and land-use *** Tier 1 and 2 experiments were carried out and were initialized using historical runs.A branch run method was used for the ensemble *** outputs were three-hourly,six-hourly,daily,and/or monthly mean values for the primary variables of the four component *** evaluation and analysis of the simulations is also *** present results are expected to aid research into future climate change and socio-economic development.

关键词： ScenarioMIP CMIP6 CAS FGOALS-g3